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Jetson Speculative Decoding

NVIDIAOfficialCreated by Jetson TeamDistributed by NVIDIA

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  • Tool integration
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  • Apache-2.0
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From the author

Add EAGLE-3 or draft-model speculative decoding to a Jetson vLLM server when TPOT is the bottleneck.
— the description by Jetson Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a configuration guide for implementing speculative decoding on NVIDIA Jetson devices running vLLM. By using a smaller draft model or an EAGLE-3 head, users can propose multiple tokens for verification in a single forward pass, effectively reducing per-token latency in single-stream workloads. The guide emphasizes the critical balance between VRAM availability and performance gains, specifically tailored for Jetson Thor and AGX Orin hardware.

What it does

  • Provides configuration templates for EAGLE-3 and draft-model speculative decoding.
  • Defines hardware-specific tuning parameters for Jetson Thor and AGX Orin.
  • Outlines memory management adjustments required to accommodate draft models.
  • Establishes a benchmarking protocol to validate performance improvements.
  • Offers troubleshooting steps for OOM errors and runtime configuration issues.

When to use it

  • When TPOT (Time Per Output Token) is the primary bottleneck in your LLM deployment.
  • When running single-stream or low-concurrency (≤2) inference workloads.
  • When you have sufficient VRAM headroom on Thor or AGX Orin devices.
  • When you need to improve token generation throughput for latency-sensitive applications.

Advantages

  • Reduces per-token latency by verifying multiple tokens in a single forward pass.
  • Provides clear, Jetson-specific tuning rules for speculative token counts.
  • Includes a structured verification process to ensure performance gains are real.
  • Helps prevent common pitfalls like OOM errors through memory utilization adjustments.

Usage examples

“How do I enable EAGLE-3 speculative decoding on my Jetson AGX Orin?”
The agent provides the specific --speculative-config JSON structure with num_speculative_tokens set to 3 and advises on lowering gpu-memory-utilization to prevent OOM.
“My vLLM server is slow at generating tokens, should I use speculative decoding?”
The agent guides you to check if your workload is low-concurrency and if you have enough VRAM, then provides the steps to benchmark before and after implementation.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/jetson-speculative-decoding/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip jetson-speculative-decoding.zip -d .agents/skills

Documentation by Jetson Team

Jetson Speculative Decoding (vLLM)

Speculative decoding lets a small "draft" model propose tokens that the target model verifies in a single forward pass, reducing per-token latency. On Jetson, the win/loss is dominated by VRAM headroom, not by the draft quality. This skill encodes the parts an LLM won't already know.

Purpose

Tune an existing Jetson vLLM deployment for faster token generation by appending the right --speculative-config and validating whether it improves single-stream decode speed.

When to use

  • TPOT/ITL is the bottleneck (TTFT is fine, output is just slow).

  • Workload is single-stream or low-concurrency (≤2). Speculation usually loses at high concurrency.

  • Jetson family is Thor or AGX Orin. Do not suggest EAGLE-3 on Orin Nano/NX — there is rarely enough VRAM headroom to host both target and draft, and you'll OOM at startup.

When NOT to use

  • High-concurrency serving (≥8): batched decode usually beats speculation; the draft model just steals VRAM.

  • Models without a published EAGLE-3 head — do not train one ad-hoc as a "fix".

  • After applying jetson-inference-mem-tune flags that already pushed --gpu-memory-utilization near the ceiling. Free at least ~2 GB first.

Prerequisites

  • A working vLLM server recipe from jetson-llm-serve.

  • Enough memory headroom for the draft model or EAGLE-3 head in addition to the target model.

  • A benchmark baseline from jetson-llm-benchmark before enabling speculation.

  • A target model with a compatible EAGLE-3 head, or a small same-family draft model for the fallback path.

Instructions

Append --speculative-config to the vllm serve command shown in jetson-llm-serve.

EAGLE-3 (preferred when a head is published for the target model):

--speculative-config '{
  "method": "eagle3",
  "model": "<eagle3-head-repo-id>",
  "num_speculative_tokens": 5,
  "draft_tensor_parallel_size": 1
}'

Draft-model (fallback — pair a small same-family model):

--speculative-config '{
  "method": "draft_model",
  "model": "<small-draft-model-repo-id>",
  "num_speculative_tokens": 4,
  "draft_tensor_parallel_size": 1
}'
Jetson-specific tuning rules
  • num_speculative_tokens: start at 5 on Thor, 3 on AGX Orin. Higher values pay off only if the draft acceptance rate is >0.6.

  • Always pair with the same vLLM runtime path used by jetson-llm-serve: upstream vLLM 0.20+ (vllm/vllm-openai:latest) or validated native vLLM 0.20+ on Thor, upstream vLLM 0.20+ on Orin JetPack 7.2 / L4T r39+, or the NVIDIA-AI-IOT vLLM image on older Orin. Do not use an Orin NVIDIA-AI-IOT vLLM image on Thor. Older runtimes may lack EAGLE-3 or the current --speculative-config shape.

  • Drop --gpu-memory-utilization by ~0.05 vs the non-speculative baseline to give the draft model headroom.

How to verify it actually helped

  1. Run jetson-llm-benchmark (vLLM path) at --concurrency 1 before and after enabling speculation.

  2. Acceptance: target ≥30% improvement in throughput_tok_s and ≥20% drop in tpot_ms_p50 at concurrency 1.

  3. If improvement is <10%, or throughput_tok_s regresses at concurrency 8, disable speculation. The draft model is costing more than it returns.

Limitations

  • Speculative decoding improves decode-heavy workloads; it does not reduce TTFT-dominated latency.

  • High concurrency can erase the benefit because continuous batching already keeps the GPU busy.

  • Orin Nano/NX usually lack enough memory headroom for both target and draft models.

  • Acceptance rate and draft overhead are model-specific, so benchmark before and after instead of assuming a speedup.

Error handling

  • If vLLM rejects --speculative-config, verify that Thor and Orin JetPack 7.2 / L4T r39+ are using vLLM 0.20+ and that older Orin is using a JetPack-matched NVIDIA-AI-IOT vLLM image; then switch back to the non-speculative serving command if the runtime still rejects it.

  • If startup OOMs, lower --gpu-memory-utilization, use a smaller draft, or disable speculation and hand off to jetson-inference-mem-tune.

  • If benchmark throughput regresses, remove --speculative-config; a bad draft path is worse than no speculation.

Hand off to

  • jetson-llm-benchmark to quantify the change.

  • jetson-inference-mem-tune if startup OOMs after enabling speculation.

Source

vLLM speculative decoding docs and the Jetson AI Lab GenAI tutorial.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Attribution and licence

Created by
Jetson Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/jetson-speculative-decoding · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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